Die Daten - Weltkulturerbe

url <- "https://raw.githubusercontent.com/Japhilko/
GeoData/master/2015/data/whcSites.csv"

whcSites <- read.csv(url) 

library(knitr)
whcSitesDat <- with(whcSites,data.frame(name_en,
                                        category))
kable(head(whcSitesDat))

Weitere Variablen WHC Datensatz

whcSitesDat2 <- with(whcSites,data.frame(name_en,category,longitude,latitude,date_inscribed,area_hectares,danger_list))

library('DT')
datatable(whcSitesDat2)

Das Ergebnis bei Rpubs

http://rpubs.com/Japhilko82/WHCdata

Notwendige Pakete

magrittr - für den Pipe Operator in R:

library("magrittr")

leaflet - um interaktive Karten mit der JavaScript Bibliothek ‘Leaflet’ zu erzeugen

library("leaflet")

Eine interaktive Karte erstellen

m <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=whcSites$lon, 
             lat=whcSites$lat, 
             popup=whcSites$name_en)
m

Die Karte zeigen

Farbe hinzu

whcSites$color <- "red"
whcSites$color[whcSites$category=="Cultural"] <- "blue"
whcSites$color[whcSites$category=="Mixed"] <- "orange"

Eine Karte mit Farbe erzeugen

m1 <- leaflet() %>%
  addTiles() %>%  
  addCircles(lng=whcSites$lon, 
             lat=whcSites$lat, 
             popup=whcSites$name_en,
             color=whcSites$color)

Die Karte zeigen

Weltkulturerbe

Die Karte abspeichern

Layers ein- und ausblenden

leaflet() %>%
  addTiles() %>%
  addMarkers(data = coffee_shops, group = "Food & Drink") %>%
  addMarkers(data = restaurants, group = "Food & Drink") %>%
  addMarkers(data = restrooms, group = "Restrooms")

Karte mit Polygonen erzeugen

library(sp)
Sr1 = Polygon(cbind(c(2, 4, 4, 1, 2), c(2, 3, 5, 4, 2)))
Sr2 = Polygon(cbind(c(5, 4, 2, 5), c(2, 3, 2, 2)))
Sr3 = Polygon(cbind(c(4, 4, 5, 10, 4), c(5, 3, 2, 5, 5)))
Sr4 = Polygon(cbind(c(5, 6, 6, 5, 5), c(4, 4, 3, 3, 4)), hole = TRUE)
Srs1 = Polygons(list(Sr1), "s1")
Srs2 = Polygons(list(Sr2), "s2")
Srs3 = Polygons(list(Sr4, Sr3), "s3/4")
SpP = SpatialPolygons(list(Srs1, Srs2, Srs3), 1:3)
leaflet(height = "300px") %>% addPolygons(data = SpP)

Beispiel US Staaten

library(maps)
mapStates = map("state", fill = TRUE, plot = FALSE)
leaflet(data = mapStates) %>% addTiles() %>%
  addPolygons(fillColor = topo.colors(10, alpha = NULL), stroke = FALSE)

Die Basiskarte ändern

• Neben der Default Basiskarte kann man auch andere Hintergründe aktivieren

m <- leaflet() %>% setView(lng = -71.0589, lat = 42.3601, zoom = 12)
m %>% addTiles()

m %>% addProviderTiles("Stamen.Toner")

Basiskarte - CartoDB

m %>% addProviderTiles("CartoDB.Positron")

Esri.NatGeoWorldMap

m %>% addProviderTiles("Esri.NatGeoWorldMap")

OpenTopoMap

m %>% addProviderTiles("OpenTopoMap")

Thunderforest.OpenCycleMap

m %>% addProviderTiles("Thunderforest.OpenCycleMap")

WMS Tiles hinzufügen

leaflet() %>% addTiles() %>% setView(-93.65, 42.0285, zoom = 4) %>%
  addWMSTiles(
    "http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi",
    layers = "nexrad-n0r-900913",
    options = WMSTileOptions(format = "image/png", transparent = TRUE),
    attribution = "Weather data © 2012 IEM Nexrad"
  )

Mehrere Layer miteinander kombinieren

m %>% addProviderTiles("MtbMap") %>%
  addProviderTiles("Stamen.TonerLines",
    options = providerTileOptions(opacity = 0.35)) %>%
  addProviderTiles("Stamen.TonerLabels")

Andere Marker benutzen

greenLeafIcon <- makeIcon(
  iconUrl = "http://leafletjs.com/examples/custom-icons/leaf-green.png",
  iconWidth = 38, iconHeight = 95,
  iconAnchorX = 22, iconAnchorY = 94,
  shadowUrl = "http://leafletjs.com/examples/custom-icons/leaf-shadow.png",
  shadowWidth = 50, shadowHeight = 64,
  shadowAnchorX = 4, shadowAnchorY = 62
)

leaflet(data = quakes[1:4,]) %>% addTiles() %>%
  addMarkers(~long, ~lat, icon = greenLeafIcon)

Cluster Optionen für Marker

leaflet(quakes) %>% addTiles() %>% addMarkers(
  clusterOptions = markerClusterOptions()
)

Ein Rechteck hinzufügen

leaflet() %>% addTiles() %>%
  addRectangles(
    lng1=-118.456554, lat1=34.078039,
    lng2=-118.436383, lat2=34.062717,
    fillColor = "transparent"
  )

Links und Quellen

• 4 Tricks zum Arbeiten mit Leaflet

• http://www.r-bloggers.com/the-leaflet-package-for-online-mapping-in-r/

• https://rstudio.github.io/leaflet/

The R-package DT

• DT: An R interface to the DataTables library

install.packages('DT')

library('DT')

exdat <- read.csv("data/exdat.csv")

datatable(exdat)

Beispiel für interaktive Tabelle

Hier ist das Ergebnis - Beispiel für eine interaktive Tabelle

Default Optionen verändern

datatable(head(exdat, 20), options = list(
  columnDefs = list(list(className = 'dt-center', targets = 5)),
  pageLength = 5,
  lengthMenu = c(5, 10, 15, 20)
))

Suchoptionen kennzeichnen

datatable(exdat, options = list(searchHighlight = TRUE), filter = 'top')

D3 Graph Gallery

• Combining the power of R and D3.js

• Five Interactive R Visualizations With D3, ggplot2, & RStudio

• Das D3 Netzwerk

Interaktive Graphiken

• R Interactive Graphics with SVG

tabplotd3

• Beispiel tabplot3d

install.packages("tabplotd3")

library(tabplotd3)
require(ggplot2)
data(diamonds)
tableplot(diamonds)

gigvis

install.packages("ggvis")

library("ggvis")
library(dplyr)

• Übersicht zu ggvis

Kochbuch für ggvis

mtcars %>% ggvis(~wt, ~mpg) %>% layer_points()

Plots mit Gruppierung

mtcars %>% 
  ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>% 
  layer_points() %>% 
  group_by(cyl) %>% 
  layer_model_predictions(model = "lm")

googleVis

install.packages("googleVis")

library(googleVis)

df <- data.frame(year=1:11, x=1:11,
                 x.scope=c(rep(TRUE, 8), rep(FALSE, 3)),
                 y=11:1, y.html.tooltip=LETTERS[11:1],                 
                 y.certainty=c(rep(TRUE, 5), rep(FALSE, 6)),
                 y.emphasis=c(rep(FALSE, 4), rep(TRUE, 7)))
plot(
  gvisScatterChart(df,options=list(lineWidth=2))
)

Click me

install.packages("devtools")
library(devtools)

install_github("clickme", "nachocab")

library(clickme)

# simple
clickme("points", 1:10)

# fancy
n <- 500
clickme("points",
    x = rbeta(n, 1, 10), y = rbeta(n, 1, 10),
    names = sample(letters, n, r = T),
    color_groups = sample(LETTERS[1:3], n, r = T),
    title = "Zoom Search Hover Click")

d3Network

install.packages("d3Network")

library(d3Network)
Source <- c("A", "A", "A", "A", "B", "B", "C", "C", "D")
Target <- c("B", "C", "D", "J", "E", "F", "G", "H", "I")
NetworkData <- data.frame(Source, Target)
d3SimpleNetwork(NetworkData, width = 400, height = 250)

## 
##         <!DOCTYPE html>
##         <meta charset="utf-8">
##         <body> 
##  <style>
## .link {
## stroke: #666;
## opacity: 0.6;
## stroke-width: 1.5px;
## }
## .node circle {
## stroke: #fff;
## opacity: 0.6;
## stroke-width: 1.5px;
## }
## text {
## font: 7px serif;
## opacity: 0.6;
## pointer-events: none;
## }
## </style>
## 
## <script src=http://d3js.org/d3.v3.min.js></script>
## 
## <script> 
##  var links = [ { "source" : "A", "target" : "B" }, { "source" : "A", "target" : "C" }, { "source" : "A", "target" : "D" }, { "source" : "A", "target" : "J" }, { "source" : "B", "target" : "E" }, { "source" : "B", "target" : "F" }, { "source" : "C", "target" : "G" }, { "source" : "C", "target" : "H" }, { "source" : "D", "target" : "I" } ] ; 
##  var nodes = {}
## 
## // Compute the distinct nodes from the links.
## links.forEach(function(link) {
## link.source = nodes[link.source] ||
## (nodes[link.source] = {name: link.source});
## link.target = nodes[link.target] ||
## (nodes[link.target] = {name: link.target});
## link.value = +link.value;
## });
## 
## var width = 400
## height = 250;
## 
## var force = d3.layout.force()
## .nodes(d3.values(nodes))
## .links(links)
## .size([width, height])
## .linkDistance(50)
## .charge(-200)
## .on("tick", tick)
## .start();
## 
## var svg = d3.select("body").append("svg")
## .attr("width", width)
## .attr("height", height);
## 
## var link = svg.selectAll(".link")
## .data(force.links())
## .enter().append("line")
## .attr("class", "link");
## 
## var node = svg.selectAll(".node")
## .data(force.nodes())
## .enter().append("g")
## .attr("class", "node")
## .on("mouseover", mouseover)
## .on("mouseout", mouseout)
## .on("click", click)
## .on("dblclick", dblclick)
## .call(force.drag);
## 
## node.append("circle")
## .attr("r", 8)
## .style("fill", "#3182bd");
## 
## node.append("text")
## .attr("x", 12)
## .attr("dy", ".35em")
## .style("fill", "#3182bd")
## .text(function(d) { return d.name; });
## 
## function tick() {
## link
## .attr("x1", function(d) { return d.source.x; })
## .attr("y1", function(d) { return d.source.y; })
## .attr("x2", function(d) { return d.target.x; })
## .attr("y2", function(d) { return d.target.y; });
## 
## node.attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });
## }
## 
## function mouseover() {
## d3.select(this).select("circle").transition()
## .duration(750)
## .attr("r", 16);
## }
## 
## function mouseout() {
## d3.select(this).select("circle").transition()
## .duration(750)
## .attr("r", 8);
## }
## // action to take on mouse click
## function click() {
## d3.select(this).select("text").transition()
## .duration(750)
## .attr("x", 22)
## .style("stroke-width", ".5px")
## .style("opacity", 1)
## .style("fill", "#E34A33")
## .style("font", "17.5px serif");
## d3.select(this).select("circle").transition()
## .duration(750)
## .style("fill", "#E34A33")
## .attr("r", 16)
## }
## 
## // action to take on mouse double click
## function dblclick() {
## d3.select(this).select("circle").transition()
## .duration(750)
## .attr("r", 6)
## .style("fill", "#E34A33");
## d3.select(this).select("text").transition()
## .duration(750)
## .attr("x", 12)
## .style("stroke", "none")
## .style("fill", "#E34A33")
## .style("stroke", "none")
## .style("opacity", 0.6)
## .style("font", "7px serif");
## }
## 
## </script>
##  </body>

sink("FirstNetwork.js") 

writeLines(d3SimpleNetwork(NetworkData), fileConn)
unlink("FirstNetwork.js")

Rook

install.packages("Rook")

HTML widgets in R

library(dygraphs)
dygraph(nhtemp, main = "New Haven Temperatures") %>% 
  dyRangeSelector(dateWindow = c("1920-01-01", "1960-01-01"))

Combo Chart

library(googleVis)
op <- options(gvis.plot.tag = "chart")
## Add the mean
CityPopularity$Mean=mean(CityPopularity$Popularity)
CC <- gvisComboChart(CityPopularity, xvar='City',
          yvar=c('Mean', 'Popularity'),
          options=list(seriesType='bars',
                       width=450, height=300,
                       title='City Popularity',
                       series='{0: {type:\"line\"}}'))
plot(CC)

## <!-- ComboChart generated in R 3.3.2 by googleVis 0.6.2 package -->
## <!-- Fri Apr 28 11:03:31 2017 -->
## 
## 
## <!-- jsHeader -->
## <script type="text/javascript">
##  
## // jsData 
## function gvisDataComboChartID15a041332b3 () {
## var data = new google.visualization.DataTable();
## var datajson =
## [
##  [
## "New York",
## 450,
## 200
## ],
## [
## "Boston",
## 450,
## 300
## ],
## [
## "Miami",
## 450,
## 400
## ],
## [
## "Chicago",
## 450,
## 500
## ],
## [
## "Los Angeles",
## 450,
## 600
## ],
## [
## "Houston",
## 450,
## 700
## ] 
## ];
## data.addColumn('string','City');
## data.addColumn('number','Mean');
## data.addColumn('number','Popularity');
## data.addRows(datajson);
## return(data);
## }
##  
## // jsDrawChart
## function drawChartComboChartID15a041332b3() {
## var data = gvisDataComboChartID15a041332b3();
## var options = {};
## options["allowHtml"] = true;
## options["seriesType"] = "bars";
## options["width"] = 450;
## options["height"] = 300;
## options["title"] = "City Popularity";
## options["series"] = {0: {type:"line"}};
## 
## 
##     var chart = new google.visualization.ComboChart(
##     document.getElementById('ComboChartID15a041332b3')
##     );
##     chart.draw(data,options);
##     
## 
## }
##   
##  
## // jsDisplayChart
## (function() {
## var pkgs = window.__gvisPackages = window.__gvisPackages || [];
## var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || [];
## var chartid = "corechart";
##   
## // Manually see if chartid is in pkgs (not all browsers support Array.indexOf)
## var i, newPackage = true;
## for (i = 0; newPackage && i < pkgs.length; i++) {
## if (pkgs[i] === chartid)
## newPackage = false;
## }
## if (newPackage)
##   pkgs.push(chartid);
##   
## // Add the drawChart function to the global list of callbacks
## callbacks.push(drawChartComboChartID15a041332b3);
## })();
## function displayChartComboChartID15a041332b3() {
##   var pkgs = window.__gvisPackages = window.__gvisPackages || [];
##   var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || [];
##   window.clearTimeout(window.__gvisLoad);
##   // The timeout is set to 100 because otherwise the container div we are
##   // targeting might not be part of the document yet
##   window.__gvisLoad = setTimeout(function() {
##   var pkgCount = pkgs.length;
##   google.load("visualization", "1", { packages:pkgs, callback: function() {
##   if (pkgCount != pkgs.length) {
##   // Race condition where another setTimeout call snuck in after us; if
##   // that call added a package, we must not shift its callback
##   return;
## }
## while (callbacks.length > 0)
## callbacks.shift()();
## } });
## }, 100);
## }
##  
## // jsFooter
## </script>
##  
## <!-- jsChart -->  
## <script type="text/javascript" src="https://www.google.com/jsapi?callback=displayChartComboChartID15a041332b3"></script>
##  
## <!-- divChart -->
##   
## <div id="ComboChartID15a041332b3" 
##   style="width: 450; height: 300;">
## </div>

• Introducing JS

install.packages("threejs")

# install.packages("threejs")
library(threejs)
z <- seq(-10, 10, 0.01)
x <- cos(z)
y <- sin(z)
scatterplot3js(x,y,z, color=rainbow(length(z)))

Links

• Google Charts

• Three little circles

• Wie schreibt man gute html widgets

• Die widgets exportieren

• R wie Javascript programmieren

• Mit Javascript in R arbeiten

• Paket rbokeh

• Interaktive Graphiken mit D3 und plotly

• Netzwerkgraphiken mit vis.js

• Das Erstellen von Ablaufdiagrammen mit mermaid

Internetresourcen und Schnittstellen nutzen

• Import von JSON-Objekten und XML Dateien (pdf | rcode)

• Die Pakete rvest und RCurl

• Webscraping

• Use Case - Scraping Wikipedia

Applikationen und Projektverwaltung mit Rstudio und git

• Shiny Apps

• R und Git

Hochperfomanter Code

• C++ Integration - Überblick über das Paket rcpp

• Überblick über Möglichkeiten des Parallel Computings - Paket parallel

Integration von Datenbanken

• Datenbanken und R

• Integration von PostgreSQL mit dem Paket RPostgreSQL

• Nutzung von MongoDB in R